Data Analytics Lab (B.Tech)

Data analysis and visualisation exercises using Python or R.

6 MonthsDuration 6Chapters22Topics

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Syllabus · पाठ्यक्रम

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01Python and R Basics 4 topics

Python lists, dictionaries, NumPy arrays
pandas Series and DataFrame
R vectors and data frames
Reading CSV and Excel files

02Data Cleaning and Pre-processing 4 topics

Handling missing values
Removing duplicates and outliers
Normalisation and standardisation
Encoding categorical variables

03Exploratory Data Analysis and Visualisation 4 topics

Summary statistics
Histogram, box plot and scatter plot
Correlation heat map
Group-by analysis

04Regression and Classification 4 topics

Simple and multiple linear regression
Logistic regression
Decision tree classifier
Evaluation: RMSE, accuracy, confusion matrix

05Clustering and Association Rules 3 topics

K-means clustering and elbow method
Hierarchical clustering
Apriori algorithm and association rules

06Big Data Tools 3 topics

HDFS commands
MapReduce word count
Hive query on a sample dataset

About

You will be able to

Load and manipulate datasets with pandas / R data frames
Clean data: missing values, duplicates, outliers, normalisation
Perform exploratory data analysis with summary statistics and plots
Build and evaluate regression and classification models
Apply K-means clustering and Apriori association rules
Run basic HDFS commands and a MapReduce word count

Who it is for

B.Tech Information Technology students of Semester 6 (AKTU)
Students of other universities studying the same subject who want unit-wise coaching

What you need

Data Analytics theory of the same semester
Python 3 with Jupyter (or R with RStudio)

Questions

Which syllabus does the Data Analytics Lab course follow?

It follows the AKTU (Dr. A.P.J. Abdul Kalam Technical University, Lucknow) B.Tech syllabus for Data Analytics Lab. If your own university's syllabus differs, the centre follows your university syllabus.

Will this help in the university examination?

Yes. Teaching is unit-wise as per the AKTU scheme, with previous-year question practice, MCQ tests and doubt sessions before the semester exam.

Which datasets are used?

Open datasets such as Iris, Titanic, house prices and market-basket data from Kaggle / UCI, or any dataset your college specifies.